LaPlaSS: Latent Space Planning for Stochastic Systems

• URL: http://arxiv.org/abs/2404.07063v1
• Date: Wed, 10 Apr 2024 14:52:35 GMT
• Title: LaPlaSS: Latent Space Planning for Stochastic Systems
• Authors: Marlyse Reeves, Brian C. Williams,
• Abstract summary: We propose a "generate-and-test" approach to risk-bounded planning for autonomous mobile agents. We use a variational autoencoder to learn a latent linear dynamics model and encode the planning problem into the latent space to generate the candidate trajectory. We demonstrate that our algorithm, LaPlaSS, is able to generate trajectory plans with bounded risk for a real-world agent with learned dynamics.
• Score: 8.529245639496274
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Autonomous mobile agents often operate in hazardous environments, necessitating an awareness of safety. These agents can have non-linear, stochastic dynamics that must be considered during planning to guarantee bounded risk. Most state of the art methods require closed-form dynamics to verify plan correctness and safety however modern robotic systems often have dynamics that are learned from data. Thus, there is a need to perform efficient trajectory planning with guarantees on risk for agents without known dynamics models. We propose a "generate-and-test" approach to risk-bounded planning in which a planner generates a candidate trajectory using an approximate linear dynamics model and a validator assesses the risk of the trajectory, computing additional safety constraints for the planner if the candidate does not satisfy the desired risk bound. To acquire the approximate model, we use a variational autoencoder to learn a latent linear dynamics model and encode the planning problem into the latent space to generate the candidate trajectory. The VAE also serves to sample trajectories around the candidate to use in the validator. We demonstrate that our algorithm, LaPlaSS, is able to generate trajectory plans with bounded risk for a real-world agent with learned dynamics and is an order of magnitude more efficient than the state of the art.

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